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Estimators of wheel slip for electric vehicles using torque and encoder measurements

机译：使用扭矩和编码器测量值估算电动汽车的轮滑

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For the purpose of regenerative braking control in hybrid and electrical vehicles, recent studies have suggested controlling the slip ratio of the electric-powered wheel. A slip tracking controller requires an accurate slip estimation in the overall range of the slip ratio (from 0 to 1), contrary to the conventional slip limiter (ABS) which calls for an accurate slip estimation in the critical slip area, estimated at around 0.15 in several applications. Considering that it is not possible to directly measure the slip ratio of a wheel, the problem is to estimate the latter from available online data. To estimate the slip of a wheel, both wheel speed and vehicle speed must be known. Several studies provide algorithms that allow obtaining a good estimation of vehicle speed. On the other hand, there is no proposed algorithm for the conditioning of the wheel speed measurement Indeed, the noise included in the wheel speed measurement reduces the accuracy of the slip estimation, a disturbance increasingly significant at low speed and low torque. Herein, two different extended Kalman observers of slip ratio were developed. The first calculates the slip ratio with data provided by an observer of vehicle speed and of propeller wheel speed. The second observer uses an original nonlinear model of the slip ratio as a function of the electric motor. A sinus tracking algorithm is included in the two observers, in order to reject harmonic disturbances of wheel speed measurement Moreover, mass and road uncertainties can be compensated with a coefficient adapted online by an RLS. The algorithms were implemented and tested with a three-wheel recreational hybrid vehicle. Experimental results show the efficiency of both methods.
机译：为了在混合动力和电动车辆中进行再生制动控制，最近的研究建议控制电动车轮的滑移率。与传统的滑动限制器（ABS）要求在关键滑动区域进行精确的滑动估计（估计在0.15左右）相反，滑动跟踪控制器需要在滑动比率的整个范围（从0到1）中进行精确的滑动估计。在几种应用中。考虑到不可能直接测量车轮的滑移率，问题在于从可用的在线数据中估计车轮的滑移率。为了估计车轮的打滑，必须同时知道车轮速度和车辆速度。多项研究提供了可以很好地估计车速的算法。另一方面，没有提出用于调节车轮速度测量的算法。的确，车轮速度测量中包括的噪声降低了打滑估计的准确性，在低速和低扭矩时干扰变得越来越明显。在此，开发了两个不同的扩展卡尔曼滑移率观测器。第一种方法使用由车速和推进轮速度的观察者提供的数据来计算滑移率。第二观察者使用原始的滑移率非线性模型作为电动机的函数。在两个观测器中都包含一个正弦跟踪算法，以排除车轮速度测量的谐波干扰。此外，质量和道路不确定性可以通过RLS在线修改的系数进行补偿。该算法是用三轮休闲混合动力汽车实施和测试的。实验结果表明了两种方法的有效性。

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